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Author : Grace Y. Yi
Publisher : Unknown
Page : 479 pages
File Size : 53,7 Mb
Release : 2017
Category : Epidemiology
ISBN : 1493966391
Author : Paul Gustafson
Publisher : CRC Press
Page : 213 pages
File Size : 52,8 Mb
Release : 2003-09-25
Category : Mathematics
ISBN : 9780203502761
Mismeasurement of explanatory variables is a common hazard when using statistical modeling techniques, and particularly so in fields such as biostatistics and epidemiology where perceived risk factors cannot always be measured accurately. With this perspective and a focus on both continuous and categorical variables, Measurement Error and Misclassi
Author : Grace Y. Yi
Publisher : Springer
Page : 479 pages
File Size : 51,5 Mb
Release : 2017-08-02
Category : Mathematics
ISBN : 9781493966400
This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems. Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods—such as likelihood and estimating function theory—or modeling schemes in varying settings—such as survival analysis and longitudinal data analysis—can result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods. This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data. Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.
Author : Grace Y. Yi,Aurore Delaigle,Paul Gustafson
Publisher : Chapman & Hall/CRC
Page : 578 pages
File Size : 55,7 Mb
Release : 2021-10-17
Category : Mathematics
ISBN : 1315101270
"Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention. The Handbook of Measurement Error provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike. Features: Provides an account of past development and modern advancement concerning measurement error problems; Highlights the challenges induced by error-contaminated data; Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error; Describes state-of-the-art strategies for conducting in-depth research. Grace Y. Yi is Professor of Statistics at the University of Western Ontario where she holds a Tier I Canada Research Chair in Data Science. She is a Fellow of the Institute of Mathematical Statistics (IMS), a Fellow of the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). She authored the monograph Statistical Analysis with Measurement Error or Misclassification (2017, Springer). Aurore Delaigle is Professor at the School of Mathematics and Statistics at the University of Melbourne. She is a Fellow of the Australian Academy of Science, a Fellow of the Institute of Mathematical Statistics (IMS), a Fellow of the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). She is a past recipient of the George W. Snedecor Award from the Committee of Presidents of Statistical Societies (COPSS) and of the Moran Medal from the Australian Academy of Science. Paul Gustafson is Professor and Head of the Department of Statistics at the University of British Columbia. He is a Fellow of the American Statistical Association, the 2020 Gold Medalist of the Statistical Society of Canada, and the author of the monograph Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments (2004, Chapman and Hall, CRC Press)"--
Author : John P. Buonaccorsi
Publisher : CRC Press
Page : 465 pages
File Size : 54,7 Mb
Release : 2010-03-02
Category : Mathematics
ISBN : 9781420066586
Over the last 20 years, comprehensive strategies for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters have emerged. Focusing on both established and novel approaches, Measurement Error: Models, Methods, and Applications provides an overview of the main techniques and illu
Author : Paul P. Biemer,Robert M. Groves,Lars E. Lyberg,Nancy A. Mathiowetz,Seymour Sudman
Publisher : John Wiley & Sons
Page : 802 pages
File Size : 49,6 Mb
Release : 2013-08-12
Category : Mathematics
ISBN : 0471692808
WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This book will be an aid to survey statisticians and to research workers who must work with survey data." –Short Book Reviews, International Statistical Institute Measurement Errors in Surveys documents the current state of the field, reports new research findings, and promotes interdisciplinary exchanges in modeling, assessing, and reducing measurement errors in surveys. Providing a fundamental approach to measurement errors, the book features sections on the questionnaire, respondents and responses, interviewers and other means of data collection, the respondent-interviewer relationship, and the effects of measurement errors on estimation and data analysis.
Author : Timothy L. Lash,Matthew P. Fox,Aliza K. Fink
Publisher : Springer Science & Business Media
Page : 200 pages
File Size : 46,9 Mb
Release : 2011-04-14
Category : Medical
ISBN : 9780387879598
Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow.
Author : Grace Y. Yi,Aurore Delaigle,Paul Gustafson
Publisher : CRC Press
Page : 648 pages
File Size : 53,8 Mb
Release : 2021-09-28
Category : Mathematics
ISBN : 9781351588591
Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention. The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike. Features: Provides an account of past development and modern advancement concerning measurement error problems Highlights the challenges induced by error-contaminated data Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error Describes state-of-the-art strategies for conducting in-depth research
Author : Philip J. Brown,Wayne A. Fuller,American Mathematical Society
Publisher : American Mathematical Soc.
Page : 262 pages
File Size : 44,7 Mb
Release : 1990
Category : Mathematics
ISBN : 9780821851173
Measurement error models describe functional relationships among variables observed, subject to random errors of measurement. This book treats general aspects of the measurement problem and features a discussion of the history of measurement error models.
Author : Raymond J. Carroll,David Ruppert,Leonard A. Stefanski,Ciprian M. Crainiceanu
Publisher : CRC Press
Page : 484 pages
File Size : 50,5 Mb
Release : 2006-06-21
Category : Mathematics
ISBN : 9781420010138
It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex
Author : James R. Carpenter,Jonathan Bartlett,Tim Morris,Angela Wood,Matteo Quartagno,Michael G. Kenward
Publisher : John Wiley & Sons
Page : 469 pages
File Size : 40,6 Mb
Release : 2023-10-09
Category : Medical
ISBN : 9781119756088
Multiple Imputation and its Application The most up-to-date edition of a bestselling guide to analyzing partially observed data In this comprehensively revised Second Edition of Multiple Imputation and its Application, a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings. With an accessible and carefully structured presentation aimed at quantitative researchers, Multiple Imputation and its Application is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find: A comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets Careful discussion of key concepts A range of examples illustrating the key ideas Practical advice on using multiple imputation Exercises and examples designed for use in the classroom and/or private study Written for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic, Multiple Imputation and its Application will also earn a place in the libraries of graduate students undertaking quantitative analyses.
Author : Christos P. Kitsos,Teresa A. Oliveira,Francesca Pierri,Marialuisa Restaino
Publisher : Springer Nature
Page : 230 pages
File Size : 41,8 Mb
Release : 2024-01-13
Category : Mathematics
ISBN : 9783031398643
This volume covers the latest results on novel methods in Risk Analysis and assessment, with applications in Biostatistics (which is providing food for thought since the first ICRAs, covering traditional areas of RA, until now), Engineering Reliability, the Environmental Sciences and Economics. The contributions, based on lectures given at the 9th International Conference on Risk Analysis (ICRA 9), at Perugia, Italy, May 2022, detail a wide variety of daily risks, building on ideas presented at previous ICRA conferences. Working within a strong theoretical framework, supporting applications, the material describes a modern extension of the traditional research of the 1980s. This book is intended for graduate students in Mathematics, Statistics, Biology, Toxicology, Medicine, Management, and Economics, as well as quantitative researchers in Risk Analysis.
Author : Joseph J. Breen,Cindy R. Stroup
Publisher : CRC Press
Page : 298 pages
File Size : 55,8 Mb
Release : 2020-08-13
Category : Science
ISBN : 9781000114720
Lead Poisoning discusses one of the most critical and preventable environmentally induced illnesses. The actual toll lead poisoning takes on society cannot be measured fully due to the "silent" nature of health effects, such as subtle intellectual deficits and neurological damage, caused by chronic low-level exposures. This book covers every major topic on the subject, including lead poisoning in children, sources of contamination, state-of-the-art sampling and analytical measurement methods, the newest studies on low-cost abatement methods, and much more. This reference is the most comprehensive presentation of issues currently available under one cover. The text is divided into three major parts. Part I provides insights from studies assessing lead exposures from paint, dust, soil, and lead battery recycling operations. The second part is a unique collection of strategic federal policy statements from the U.S. EPA, HUD, and HEW-CDC. It details the National Implementation Plan as well as a local government's efforts to provide low-cost effective risk communication and public outreach to the community. The next part offers seven chapters on analytical issues in the measurement of lead in blood, paint, dust, and soils. Part IV, Sampling Methods and Statistical Issues, rounds out the technical portion of the volume. The relationships among lead levels in biological and environmental media are investigated and the interpretive problems discussed. The use of multi-element analysis of environmental samples as an approach to investigate sources is described. The book finishes with its most unique feature-OPPT's Check Our Kids for Lead Program, one organization's effort to empower its employees to make a personal difference in confronting the problem of lead poisoning in children. The Program serves as a model for other government organizations (federal, state, and local), university and community organizations, and corporations to educate them and take personal and corporate responsibility for addressing this important and environmental health problem.
Author : Agency for Health Care Research and Quality (U.S.)
Publisher : Government Printing Office
Page : 204 pages
File Size : 46,8 Mb
Release : 2013-02-21
Category : Medical
ISBN : 9781587634239
This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
Author : Paul P. Biemer,Edith de Leeuw,Stephanie Eckman,Brad Edwards,Frauke Kreuter,Lars E. Lyberg,N. Clyde Tucker,Brady T. West
Publisher : John Wiley & Sons
Page : 624 pages
File Size : 42,9 Mb
Release : 2017-02-13
Category : Social Science
ISBN : 9781119041696
Featuring a timely presentation of total survey error (TSE), this edited volume introduces valuable tools for understanding and improving survey data quality in the context of evolving large-scale data sets This book provides an overview of the TSE framework and current TSE research as related to survey design, data collection, estimation, and analysis. It recognizes that survey data affects many public policy and business decisions and thus focuses on the framework for understanding and improving survey data quality. The book also addresses issues with data quality in official statistics and in social, opinion, and market research as these fields continue to evolve, leading to larger and messier data sets. This perspective challenges survey organizations to find ways to collect and process data more efficiently without sacrificing quality. The volume consists of the most up-to-date research and reporting from over 70 contributors representing the best academics and researchers from a range of fields. The chapters are broken out into five main sections: The Concept of TSE and the TSE Paradigm, Implications for Survey Design, Data Collection and Data Processing Applications, Evaluation and Improvement, and Estimation and Analysis. Each chapter introduces and examines multiple error sources, such as sampling error, measurement error, and nonresponse error, which often offer the greatest risks to data quality, while also encouraging readers not to lose sight of the less commonly studied error sources, such as coverage error, processing error, and specification error. The book also notes the relationships between errors and the ways in which efforts to reduce one type can increase another, resulting in an estimate with larger total error. This book: • Features various error sources, and the complex relationships between them, in 25 high-quality chapters on the most up-to-date research in the field of TSE • Provides comprehensive reviews of the literature on error sources as well as data collection approaches and estimation methods to reduce their effects • Presents examples of recent international events that demonstrate the effects of data error, the importance of survey data quality, and the real-world issues that arise from these errors • Spans the four pillars of the total survey error paradigm (design, data collection, evaluation and analysis) to address key data quality issues in official statistics and survey research Total Survey Error in Practice is a reference for survey researchers and data scientists in research areas that include social science, public opinion, public policy, and business. It can also be used as a textbook or supplementary material for a graduate-level course in survey research methods.